Llama 4 Scout vs Cohere Rerank 3.5

Compare Llama 4 Scout and Cohere Rerank 3.5: pricing, performance, context window, latency, and best use cases. Side-by-side comparison on XALEN.

Updated 2026-05-21 · By Abhishek Raj · Our methodology

All Meta models All Cohere models What is an LLM API? Python Quickstart What is inference?
Feature Llama 4 Scout Cohere Rerank 3.5
CategoryOpen SourceReranking
Parameters109B (17B active)~600M
Context Window512K4K
Input Price$0.05/1M tokens$0.002/search/1M tokens
Output Price$0.08/1M tokensN/A/1M tokens
Latency~350ms~25ms

Choose Llama 4 Scout when:

  • ✓ Classical text analysis
  • ✓ Long content
  • ✓ Multi-turn
Key Strengths:

512K context, MoE efficiency, Strong multilingual

Choose Cohere Rerank 3.5 when:

  • ✓ Search reranking
  • ✓ RAG improvement
  • ✓ Result quality
Key Strengths:

Higher quality, Multilingual, Fast

Verdict: Llama 4 Scout vs Cohere Rerank 3.5

For cost efficiency, Cohere Rerank 3.5 wins at $0.002/search/1M input tokens. For speed, Cohere Rerank 3.5 is faster at ~25ms. Llama 4 Scout excels at Classical text analysis while Cohere Rerank 3.5 is better for Search reranking. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.

Detailed Analysis

Pricing Comparison

Llama 4 Scout costs $0.05/1M input tokens and $0.08/1M output tokens. Cohere Rerank 3.5 costs $0.002/search input and N/A output. Cohere Rerank 3.5 is 25.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Llama 4 Scout has a 512K context window with ~350ms latency. Cohere Rerank 3.5 offers 4K context at ~25ms. Llama 4 Scout has the larger context window.

Best For

Llama 4 Scout (Open Source) is optimized for: Classical text analysis, Long content, Multi-turn. Cohere Rerank 3.5 (Reranking) works best for: Search reranking, RAG improvement, Result quality.

Try Both on XALEN

Both models are available through XALEN's OpenAI-compatible API. Switch between them by changing the model parameter:

from xalen import XALEN

client = XALEN(api_key="xln_test_YOUR_KEY")

# Use Llama 4 Scout
response_a = client.chat.completions.create(
    model="llama-4-scout",
    messages=[{"role": "user", "content": "Your question here"}]
)

# Use Cohere Rerank 3.5
response_b = client.chat.completions.create(
    model="rerank-v3-5",
    messages=[{"role": "user", "content": "Your question here"}]
)

Start Building with XALEN

200+ AI models. One API. Pay-as-you-go.

Get API Key Try in Playground

Frequently Asked Questions

Which is better, Llama 4 Scout or Cohere Rerank 3.5?

Llama 4 Scout (Open Source, 109B (17B active)) offers 512K context. Cohere Rerank 3.5 (Reranking, ~600M) offers Higher quality. Choose Llama 4 Scout for Classical text analysis or Cohere Rerank 3.5 for Search reranking.

How much does Llama 4 Scout cost vs Cohere Rerank 3.5?

Llama 4 Scout: $0.05/1M input, $0.08/1M output. Cohere Rerank 3.5: $0.002/search/1M input, N/A/1M output. Both available on XALEN with batch processing at 50% discount.

Can I use both models on XALEN?

Yes. XALEN provides 200+ models through a single OpenAI-compatible API. Switch between Llama 4 Scout and Cohere Rerank 3.5 by changing the model parameter. No code changes needed.

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Last updated: 2026-05-21. Pricing and specifications may change. Check pricing page for latest rates.